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While generative adversarial networks (GANs) have revolutionized machinelearning, a number of open questions remain to fully understand them andexploit their power. One of these questions is how to efficiently achieveproper diversity and sampling of the multi-mode data space. To address this, weintroduce BasisGAN, a stochastic conditional multi-mode image generator. Byexploiting the observation that a convolutional filter can be well approximatedas a linear combination of a small set of basis elements, we learn aplug-and-played basis generator to stochastically generate basis elements, withjust a few hundred of parameters, to fully embed stochasticity intoconvolutional filters. By sampling basis elements instead of filters, wedramatically reduce the cost of modeling the parameter space with no sacrificeon either image diversity or fidelity. To illustrate this proposedplug-and-play framework, we construct variants of BasisGAN based onstate-of-the-art conditional image generation networks, and train the networksby simply plugging in a basis generator, without additional auxiliarycomponents, hyperparameters, or training objectives. The experimental successis complemented with theoretical results indicating how the perturbationsintroduced by the proposed sampling of basis elements can propagate to theappearance of generated images.

Stochastic Conditional Generative Networks with Basis Decomposition